Energy distribution system and related methods, devices, and systems

ABSTRACT

Aspects of the disclosure are directed to an autonomous energy distribution network including a plurality of solar tracker devices configured to receive solar energy and transform the solar energy into electrical energy, where each of the solar tracker devices is directly connected to a node in a power distribution grid. The network also includes a node manager configured to receive status information from the solar trackers, where the status information includes information regarding the state of the node to which each of the solar tracker device are directly connected.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Provisional ApplicationNo. 61/752,922, filed Jan. 15, 2013, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The following disclosure relates generally to energy distributionsystems. Some embodiments, for example, are directed to methods formanaging information and/or operation of power or communication nodesconnected to end-users, distribution grids, and/or power generators.

BACKGROUND

In a typical energy distribution system, a power plant produces energywith a power generator, such as a coal or gas fired generator, ahydro-powered generator, or a nuclear-powered generator. Power is thentransmitted to an end user over a transmission grid. The transmissiongrid, in turn, supplies this power to a local distribution grid whichsupplies the power to end users via low-voltage transmission lines,substations, distribution circuits, etc. A utility company can meter thepower at the end-user's premises to determine how much power has beenconsumed.

One problem with traditional energy distributions is they employantiquated transmission and distribution grids. This makes it difficultand cost prohibitive for utility companies to bring new and alternativepower generators online, such as wind, solar, geothermal, etc. Anotherproblem with these systems is that they centralize power distribution,which gives utility companies a market monopoly. Thus, many utilitycompanies are reluctant to improve their power distributioninfrastructure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that includes a node managerconfigured in accordance with an embodiment of the present technology.

FIG. 2A is an isometric view of a solar tracker system configured inaccordance with an embodiment of the present technology.

FIG. 2B is a side view of a tracker and a tracker assembly configured inaccordance with embodiments of the present technology.

FIG. 2C is a top plan view of a base configured in accordance withembodiments of the present technology.

FIG. 2D is a partial backside view of a tracker assembly configured inaccordance with an embodiment of the present technology.

FIG. 2E illustrates a method of assembling the tracker assemblyconfigured in accordance with an embodiment of the present technology.

FIG. 3A is a block diagram illustrating tracker electronics configuredin accordance with an embodiment of the present technology.

FIG. 3B is a flow diagram illustrating a routine performed by a nodemanager in accordance with an embodiment of the present technology.

FIG. 3C is a flow diagram illustrating a routine of a learning processemployed by a node manager in accordance with an embodiment of thepresent technology.

FIG. 4A is a diagram of an energy network configured in accordance withan embodiment of the present technology.

FIG. 4B is a network diagram illustrating a cloud-based networkedarrangement of node managers configured in accordance with an embodimentof the present technology.

FIG. 4C is an diagram of a controller center arranged to operate variousaspects of a microgrid in accordance with an embodiment of the presenttechnology.

FIG. 4D is a diagram of a microgrid licensing/investing/purchasingscheme configured in accordance with an embodiment of the presenttechnology.

FIGS. 5A-E illustrate various example user interfaces that can be usedwith a node manager in accordance with an embodiment of the presenttechnology.

FIG. 6 is a flow diagram illustrating an internodal bidding scheme inaccordance with an embodiment of the present technology.

FIG. 7 show various system logs than can be generated by a node managerduring a single day of operation of a solar tracker in accordance withan embodiment of the present technology.

DETAILED DESCRIPTION

As described in greater detail below, the technology disclosed hereinrelates to energy systems and, in particular. to a node managerconfigured to manage information and/or operation of various aspects ofan energy distribution system.

FIG. 1 is an overview block diagram showing the operationalrelationships of a node manager 10 in accordance with an embodiment ofthe present technology. In general, the node manager 10 is configured tomanage information and/or operation at one or more nodes in a powerdistribution system, such as power distribution nodes (e.g., asubstation), end-user user nodes (e.g., a residential, commercial,governmental, or other suitable users), and/or communication nodes(e.g., a server, database, etc.).

In one aspect, the node manager 10 communicates with or is integratedwith one or more nodal equipment 12 (e.g., nodal devices) at or in thevicinity of a node. As described in greater detail below, the nodalequipment 12 can include, for example, energy equipment (e.g., a solararray), auxiliary equipment (e.g., a load balancer), and/or networkequipment (e.g., a database).

In another aspect, the node manager 10 operates to receive nodalinformation 14. In one embodiment, the nodal information 14 can includeinformation relevant to a particular node or a number of nodes. Forexample, the resource information can relate to power demand, weatherforecasts, and/or the ambient environment in the vicinity of the nodaldevices.

In yet another aspect, the node manager 10 operates to derive nodalanalytics 16 based on the nodal information 14. In one embodiment, thenode manager 10 can derive the nodal analytics 16 based on variouscorrelations in the nodal information 14. For example, the node manager10 can correlate power demand with the weather forecast and/or theambient environment. In some embodiments, the node manager 10 generatesnodal analytics 16 based on various rules. The node manager 10 can usethese rules to develop new or modified correlations, rules, and/or otheranalytics.

In still yet another aspect, the node manager 10 operates to generatenodal knowledge 18 based on the nodal information 12 and the nodalanalytics 16. The nodal knowledge 18, for example, can allow a powerutility to predict power demand based on a weather forecast. In someembodiments, the nodal knowledge 18 can be bought, sold, or licensedaccording to a variety of transaction models. In other embodiments, thenodal knowledge 18 can be used for auditing purposes.

In general, the nodal equipment includes devices, apparatuses, systems,etc. that incorporate a node manager. Nodal equipment 12 can be located,for example, at a power distribution node (e.g., a substation), anend-user user node (with e.g., a residential, commercial, governmental,etc.), and/or a communication node (e.g., a server, database, etc.). Insome embodiments the nodal equipment 12 operates independently of a nodemanager. In other embodiments, the nodal equipment 12 allows a nodemanager to handle nodal information, analytics, and/or knowledge. Asdescribed in greater detail below, the nodal equipment 12 can includeenergy equipment, auxiliary equipment, and/or network equipment.However, nodal equipment 12 can include other types of equipment,devices, apparatus, etc. For example, the nodal equipment 12 can includea mobile phone, a personal computer, and/or a remote controller devicethat incorporate a node manager.

Nodal energy equipment generally includes equipment that is configuredto supply and/or store energy (e.g., with a bank of batteries, fuelcells, compressed air, etc.). Nodal energy equipment can include, forexample, conventional generators (e.g., coal, gas-fired, hydro-powered,nuclear, etc.) and/or renewable energy generators (solar, wind,geothermal, hydro-powered, etc.). In some embodiments, nodal energyequipment can both deliver and store energy. For example, in a smartgrid, an electric or hybrid car can receive and store energy from thegrid and provide energy back to the grid to meet peak demand, loadbalance, etc.

As described herein, nodal energy equipment is described in the contextof a solar tracker system or equipment 200 (“tracker 200”; FIG. 2A). Inother embodiments, however, other types of nodal energy equipment arepossible. Further, some aspects of the tracker 200 can be configured toprovide other advantages. For example, the mechanical, operational, aswell as the other aspects of the tracker 200 can provide certainadvantages relevant to equipment for harvesting solar energy generally.

FIG. 2A is an isometric view of a tracker 200 configured in accordancewith an embodiment of the present technology. The tracker 200 includes atracker assembly 203 and tracker electronics 205 (shown schematically)operably coupled to the tracker assembly 203. The tracker assembly 203includes a base 210, a rotatable frame 220, and a pivoting frame 230suitable to carry a solar panel array 206 (e.g., one or more solarpanels of photovoltaic cells). Various aspects of the base 210, therotatable frame 220, the pivoting frame 230 and associated motor(s) andactuator(s) are described in greater detail with reference to FIGS.2A-2E together.

FIG. 2B is a side view of a tracker 200 and tracker assembly 203 andFIG. 2C is a top plan view of the base 210 configured in accordance withembodiments of the present technology. In reference to FIGS. 2B and 2Ctogether, the base 210 can include a track member 212 supported by aplurality of foot plates 214. In one embodiment, the track member 212can be a frame (e.g., metal tubing frame) having a symmetrical (e.g.,round, circular, etc.) shape with an outer circumference C₁. The footplates 214 can, for example, be metal footings configured to support thetrack member 212 above the ground or other surface and to anchor thetracker 200 to the ground or other surface. The foot plates 214 can besecured to the track member 212 around the circumference C₁ of the trackmember 212. In some embodiments, additional support members 216 (e.g.,angled support bars or straps) can couple the track member 212 to thefoot plates 214.

Referring to FIG. 2C, the base 210 can also include a motor 240 (e.g., aslew motor; drawn in phantom) on which a base member 217 can be fixed.The motor 240 can be configured with support structures (not shown) andconfigured to rotationally turn the base member 217 around a Z-axis(e.g., azimuth, FIG. 2A) with respect to the track member 212. In oneparticular embodiment, the motor 240 can include a slew motor fromKinematics Manufacturing, Inc., of 21410 N. 15^(th) Lane, 104, Phoenix,Ariz. 85027. The base member 217 includes radially extending arms 218coupled thereto. The radially extending arms 218 can include wheels 219(drawn in phantom) for engaging and moving along the track member 212.For example the individual wheels can be attached at a distal end 215 ofeach arm 218 such that each individual arm 218 has an arm length L₁ thatextends between the base member 217 and the track member 212. Also, thewheels can ride on a round tube to minimize friction and minimizedirt/dust accumulation on the track. Minimal surface contact between thewheels and the track allows wheels to cut grass or other plants thatgrow over the track, negating the need to significantly alter thelandscape surrounding the tracker. The wheels can be made from a pipesurrounding low friction bearings to minimize the torque on thecentralized slewing drive, allowing the use of a smaller, less expensiveslewing drive. The wheels can also be made from a pipe long enough tostill contact the track if any settling or anomalies occur that causethe track to be not perfectly circular.

The motor 240 can drive rotation (e.g., in either direction around theZ-axis) of the base member 217 (e.g., using a drive shaft; not visiblein FIG. 2C) such that the wheels 219 fixed to the radially extendingarms 218 move along the track member 212. In some embodiments, the motor240 and/or drive shaft can include bearings or other friction-reducingmembers or coatings like Teflon®.

FIG. 2D is a partial backside view of a tracker assembly 203 configuredin accordance with an embodiment of the present technology. Referring toFIGS. 2B and 2D together, the tracker assembly 203 also includes arotatable frame 220 having towers 222 a and 222 b (referred to togetheras 222) coupled to the base 210 (e.g., to the arms 218). Each tower 222includes a plurality of frame members 224 interconnected by bars 226(e.g., such as metal tubing). The frame members 224 can be coupled, atone end (e.g., a first end), to the arms 218 of the base 210. The framemembers 224 can culminate (e.g., at a second end) at a tower peak 225 aand 225 b where a hinge 228 is provided for pivotally attaching thepivoting frame 230. The rotatable frame 220 can also include theactuator 250 (as best seen in FIG. 2B), such as a linear actuator) forengaging and moving (e.g., pivoting) the pivoting frame 230 with respectto the rotatable frame 220 and around the X-Y axis (FIG. 2A). Theactuator 250 can be coupled to the rotatable frame 220 and move withrespect to the rotatable frame via pins 251, 252 (only the pin 251 isvisible in FIG. 2D) around which the actuator 250 can pivot (e.g., pivotabout 90°). In one particular embodiment, the actuator 250 can include alinear actuator from Linak, Inc., of 2200 Stanley Gault Parkway,Louisville, Ky. 40223.

The pivoting frame 230 can be configured to carry, orient, position, orotherwise hold a solar panel array 206 (e.g., via a unistrut attachment253). The solar panel array 206 can comprise a plurality of photovoltaiccells suitable for converting solar energy into electrical energy. Inone particular embodiment, the solar panel array 206 can include one ormore solar panels (e.g., 240 Watt) from SunPower Corporation, of 77 RioRobles, San Jose, Calif. 95134. As described above, the pivoting frame230 can be pivotally coupled to the rotatable frame 220 via the hinges228.

In one aspect, the tracker assembly 203 is configured to utilize thesolar panel array 206 to convert solar energy into electrical energy.During operation, the tracker assembly 203 can receive control signals(FIG. 2A) to move, adjust and position the solar panel array 206 in adesired location and orientation for collecting solar energy. Forexample, the rotatable frame 220 is arranged with the base 210 such thatactuation of the motor 240 turns or rotates the base member 217 and arms218 in a manner that translates rotation of the rotatable frame 220around the Z-axis to position azimuth. Additionally, the actuator 250receives control signals to position the pivoting frame 230 in a mannerthat the pivoting frame 230 and the rotatable frame 220 togetherposition zenith. For example, the tracker electronics 205 cancommunicate via the control signals the desired azimuth and zenithangles for positioning (e.g., via the motor 240 and the actuator 250)the solar panel array 206. As such, the tracker 200 is able to positionthe rotating, pivoting tracker assembly 203 and to generate electricalenergy corresponding to the relative position of the solar panel array206.

In some embodiments, the tracker assembly 203 can transmit the convertedelectrical energy directly to the tracker electronics 205. In otherembodiments, the tracker assembly 203 may transmit at least a portionthe electrical energy to other components of the tracker 200 or toremote components. For example, the tracker assembly 203 may transfer aportion of the electrical energy directly to a bank of batteries (notshown) for reserve power.

As described above, the tracker assembly 203 is configured to receivecontrol signals from the tracker electronics 205. In response to somecommands, the tracker assembly 203 positions the solar panel array 206by changing the orientation of the rotatable frame 220 and the pivotingframe 230 via the combination of associated motor(s) (e.g., motor 240)and actuator(s) (e.g., actuator 250). In the embodiment illustrated inFIGS. 2A-2D, for example, the tracker assembly 203 positions the solarpanel array 206 by rotation about a Z-axis (e.g., azimuth) incombination with rotation about an X-Y axis (e.g., in the X-Y plane). Inresponse to other commands, the tracker assembly 203 sets operationalparameters. For example, the tracker assembly 203 can enable and/ordisable the operation of certain cells in the solar panel array 206 inresponse to the control signals.

In another aspect, the tracker assembly 203 is configured to providestatus information to the tracker electronics 205. The statusinformation can include, for example, present orientation of therotatable frame, the pivoting frame 230, and/or the solar panel array206. The status information can also include information about theambient (e.g., of the solar panel array 206, the output voltage (orcurrent) of the solar panel array 206 (or individual cells therein)),operational aspect of the motor and/or actuator (e.g., encoder position,overheat detection, speed sensors, etc.), and ambient information (e.g.,humidity, sunlight, temperature, wind speed, etc.) in the vicinity ofthe tracker assembly 203. Status information can also relate to theoverall status of the tracker, such as whether maintenance is required(e.g., when a motor has malfunctioned, a circuit board needs to bereplaced, etc.).

Additionally the base 210 supports stable mounting of the solar panelarray 206 in a fail safe manner. For example, the tracker 200 caninclude instructions for detection of imbalances and/or other mechanicalissues that would create tipping or other unstable scenarios inconventional trackers. These imbalances and other issues could beaddressed rapidly by the tracker assembly 203 by moving the solar panelarray (e.g., pivoting frame 230) into a stable position (e.g.,horizontal position, vertical position, in-line with a wind direction,etc.) in real-time.

In one embodiment, many of the sensors 257 a-257 e (referred to togetheras 257) can be located at a sensor box 255 that can be attached at ornear the pivoting frame 230 or, in other embodiments, near to theactuator 250 (e.g., linear actuator). The sensors 257 can be located atthe interior and/or exterior of the sensor box 255 and can include, forexample, a heat sensor 257 a (e.g., for detecting a temperature readingat the solar panel array 206 or elsewhere), a light sensor 257 b, anaccelerometer 257 c, a compass sensor 257 d (e.g., for detecting tiltazimuth), and a position sensor 257 e (e.g., for detecting pivotzenith). Other sensors 257 are contemplated for including in the sensorbox 255. For example, vibration sensors, moisture sensors, clocks,timers, etc. can also be included. By positioning all of the sensors 257at a common location (e.g., within a common housing or box 255), all ofthe sensors 257 and/or other related data collection devices can bemounted at the same time. Also by positioning the sensor box 255 at ornear the solar panel array 206 or, alternatively, near the actuator 250,the sensors 257 that detect position, velocity, etc. are appropriatelypositioned to measure the rotating and pivoting motion of the trackerassembly 203. Similar to the sensor box 255, the tracker electronics 205can be located within a common housing or box 258 (e.g., a secure(lockable), weather proof box) that can be mounted at or near thetracker assembly 203.

Other aspects of the present technology are directed to methods ofassembling the tracker assembly 203. In general, methods in accordancewith an embodiment of the present technology are suited for quickassembly. FIG. 2E illustrates a method 260 of assembling the trackerassembly 203 in accordance with an embodiment of the present technology.The method 260 of assembling a tracker assembly 203 includes preparing adeployment site by digging footing holes in the ground, or in otherembodiments (e.g., roof top assembly applications), preparing supportstructures to couple foot plates 214 thereto (block 262). In someembodiments, the deployment site does not need to be level. The method260 can also include assembling and leveling the base 210 at thedeployment site (block 264). Assembly of the base 210 can include, forexample, assembling the track member 212. In one embodiment, the trackmember 212 may be provided as 4 arcuate pieces that can be coupled toform a circular track member 212. Assembly of the base 210 can alsoinclude attaching a plurality of foot plates 214 to the track member 212and, in some embodiments, further securing the foot plates to the trackmember with support members 216. Assembly of the base 210 may furtherinclude providing the base member 217 and attaching the plurality ofradially extending arms 218 to the base member in a manner where thearms engage the track member with wheels 219. Once assembled, the base210 can be secured to the deployment site via attachment of the footplates 214 via poured concrete or mechanical coupling (e.g., bolts,screws, pins, etc.) and leveled. For example, the foot plates 214 can beset in the holes in the ground or attached via other retainingstructures (bolts, screws, brackets, etc.) in a manner that levels thebase 210 regardless of condition of the site.

The method 260 can continue with assembling the rotatable frame 220(e.g., assembling the towers 222, mounting the towers on the base 210via mechanical coupling (screws, bolts, etc.), attaching the hinges 228to the towers, etc.) and connecting the motor 240 (e.g., slew motor) tothe base 210 for rotating the rotatable frame 220 (block 266). Themethod can further include attaching the actuator 250 to the rotatableframe 220 with the pins 251, 252 (block 268) and mounting the pivotingframe 230 to the hinges 228 of the rotatable frame (block 270). Inoperation, the actuator 250 (e.g., a linear actuator) is positioned suchthat the actuator can engage a backside of the pivoting frame 230 tomove the pivoting frame around the X-Y axis created by hinge points.

If not already completed, solar panels can be attached to the pivotingframe 230 in an array. For example, solar panels can be attached andcarried by the pivoting frame in a 6×6 solar panel array 206 as shown inFIG. 2A. Other solar panel array configurations are contemplated. Themethod 260 can also include installing a lockable and/or weatherproofelectronics box 258 (e.g., for housing an electronic control module ortracker electronics 205) (block 272). The method 260 can further provideat block 274 assembling one or more wiring harnesses (e.g., having quickconnect/disconnect cables) to facilitate electrical connections with themotor 240, actuator 250, sensor box 255, and solar panel array 206 aswell as other components, such as an array of batteries (not shown).Further assembly and startup steps are also contemplated. For example,the method 260 could include completing assembly and installation of thetracker assembly 203 (block 276) and initializing a startup calibrationprotocol (block 278).

Accordingly, in contrast to conventional trackers that have to have alarge center hole dug for deep post installation, installation of thetracker assembly 203 can be done with minimal equipment (e.g., a wrench,stepladder, etc) and with minimal effort (e.g., an average sizedperson). In one embodiment the components of the tracker assembly 203can be sized to meet Occupational Safety and Health Act (OSHA)requirements such that a single person of average size can carry,manipulate and/or otherwise assemble the components of the trackerassembly 203. For example, the method 260 can be performed by one or twopersons of average size without the use of a crane or other specialequipment to assemble and deploy the tracker assembly 203. Many of thecomponents of the tracker assembly 203 can be off-the-shelf components(e.g., slew motor, linear actuator, fabricated metal components) andcould be relatively light-weight to facilitate ease of assembly at adesired site.

The tracker 200, the tracker assembly 203 and/or portions thereof may beassembled and distributed as kits. The kits can include tracker and/ortracker assembly components and instructions for assembling, installing,and/or initiating use of the tracker assembly 203. For example, the kitmay include all metal or other fabricated components for building andassembling the base 210, the rotatable frame 220, and pivoting frame 230(e.g., frame structures, bolts and other coupling devices, motors,electronic components, wiring harness, sensor box, etc.). As describedabove, the components can be sized and of suitable materials to meetOSHA requirements so that an individual person of average size can usethe kit to assemble the tracker assembly. Additionally, the kit caninclude assembly instructions (written instructions, video explanations,computer simulations, etc), such as, for example, instructions on how toperform the method 260 described above. The kit may also include otherinstructions, for example, instructions on operation, maintenance and/orrepair of the tracker 200 and/or tracker assembly 203. In someinstances, the kit may also include one or more solar panels to mount inthe pivoting frame 230.

FIG. 3A is a block diagram of the tracker electronics 205. In theillustrated embodiment the tracker electronics 205 include a CPU 302(central processing unit) including at least one programmed processorconfigured with memory to operate as a node manager 303. The CPU 302 canalso include interfaces and other components for communication over anetwork and/or with other devices (e.g., via a Mod Bus). The trackerelectronics 205 further include a wireless communication component 305,controllers 307, and inverter circuits 308. The wireless communicationcomponent 305 can be configured to provide a direct wireless link forthe CPU in addition to or in lieu of standard networking capabilities.The controllers 307 are configured to communicate control signals to thetracker assembly 203 to control the motor 240 and the actuator 250. TheInverters 308 are configured to receive solar generated electricity andconvert the energy into AC form. The inverters 308 can supply the DCvoltage to first and/or second power line interfaces 310 a, 310 b. Theinverters 308 can also convert stored energy at a battery bank 312 intoAC form. In some embodiments, the inverters can convert AC power fromthe grid or microgrid into DC form suitable for charging the batteriesat the battery bank 312 and/or for powering at least a portion of thetracker electronics 205. In some embodiments, the tracker electronics205 can be powered by the solar energy collected by the solar panelarray 206. In still other embodiments the battery bank 312 can beconfigured to provide power to the grid or the microgrid.

In operation, the tracker electronics 205 are configured to control thetracker assembly 203 and to receive status from the tracker assembly. Inone embodiment, a user can directly control aspects of the tracker 200.For example, the user can directly connect with the CPU 302 (e.g., via aUSB link, wireless link, and/or radio link) to, e.g., control the motor240 and the actuator 250 and/or to receive status from the individualsensors at the sensor box.

In other embodiments, the node manager 303 can be incorporated into theCPU 302 to provide control signals and/or receive status signals. In oneaspect of operation, the node manager 303 passively collects informationbut does not act on the information. That is, the node manager 303 doesnot operate (i.e., control) the nodal equipment. In another mode ofoperation, the node manager 303 can at least partially operate the nodalequipment.

FIG. 3B is a flow diagram illustrating a routine 320 performed by thenode manager 303. In the illustrated embodiment, the routine 320 isperformed by a node manager 303 at the CPU 302 of the trackerelectronics 205. In other embodiments, however, the node manager 303 canbe incorporated at other portions of the tracker electronics 205, suchas at a logic controller. Further, in other embodiments, the routine 320can be executed at other types of nodal equipment, including other typesof energy′ equipment (auxiliary).

At block 321, the routine 320 applies one or more rules that dictate, atleast in part, the operation of the node manager 303. As described ingreater detail below, these rules can be based on nodal knowledge. Insome embodiments, the rules can give the node manager 303 certaincontrol over the tracker electronics 205. For example, the node manager303 can be given at least partial control over the motor 240 (to orientazimuth) and/or the actuator 250 (to orient zenith). In one embodiment,the rule would dictate that the node manager 303 automatically decreasethe span range of the pivoting frame in certain wind conditions.

In another embodiment, a first rule may dictate that the node manager303 disconnect from the main grid when power generation at the grid isunstable (e.g., spikes) beyond a certain threshold of power stability.As described in greater detail below, the first rule may be based on aseries of test rules and nodal analytics that arrived at this particularthreshold. In some embodiments, a second rule may work in combinationwith the first rule to dictate when the tracker 200 should connect tothe microgrid. For example, the second rule could dictate to the nodemanager 303 to connect the battery bank 312 to the microgrid when thebattery is holding a sufficient amount of charge, or to keep the batterybank disconnected until the battery bank has the appropriate amount ofcharge. The rule could be based on nodal knowledge (i.e., nodalknowledge 18, FIG. 1)) related to a weather model in response to a rulerelated to a predicted weather plan.

In some embodiments, the rules can be “birth certificate” rules that setthe initial operating behavior of the node manager 303. In oneembodiment, a birth certificate can be loaded when the tracker 200 isbeing assembled. When the tracker 200 is operational, the birthcertificate can dictate that the node manager 303 self-calibrate thetracker assembly 203. By contrast, conventional tracker-type devices canbe difficult to set up in the field because they can require complicatedand time-intensive calibration procedures. For example, installers needto manually align these devices for compass heading and manually levelthe devices with, e.g., the ground.

However, the birth certificate can provide rules that instruct thetracker 200 to find proper compass direction using a GPS sensor. Inanother embodiment, the birth certificate can provide a rule that thenode manager uses to auto-level the tracker assembly. A tracker can beoff level if the foot plates are improperly installed or if the groundshifts. The birth certificate rule can provide tuning adjustments thatcompensate for any out of level alignment of the foot plates. Inparticular, the birth certificate rule can instruct the motor 240 or theactuator 250 to operate in a way that compensates for out of levelalignment. In another embodiment, the sensors 257 from the sensor box255 can be used, for example, to detect the position of the sun, GPSlocation, etc., which could also be useful for initial calibration.

At block 322, the routine acquires nodal information 14 (FIG. 1) bysensing, detecting, requesting, or otherwise acquiring nodalinformation. For example, the routine 320 can sense ambient conditions,request information over a network, or be pushed information without arequest. The nodal information 14 can relate to, for example, ambientconditions (e.g., barometric pressure, humidity, sunlight, wind speed,temperature, etc.), operating conditions (e.g., operational status ofsensors, motors, grid signal quality, battery capacity etc.), and/orperformance (e.g., array output power, conversion efficiency, etc.). Theroutine 320 can acquire nodal information 14 using, for example, thesensor box 255, network communications, and/or the wireless or radionetwork 305 (FIG. 3A).

In many embodiments, the routine 320 can time/date stamp and GPS stampthe nodal information 14. In this way the tracker 200 or a remote devicecan use the nodal information 14 to derive spatial temporal analyticsinformation. As described in greater detail below with reference toFIGS. 4A-4D, the nodal information 14 can be utilized in a variety ofnetwork configurations, such as cloud networks, local networks, networkislanding, etc.

At decision block 323, the routine 320 determines whether to push (e.g.,transmit) some or all of the nodal information 14 to at least one othernodal device (e.g., nodal equipment 12, FIG. 1), such as a remoteserver, a local computer, a maintenance technicians' portable computingdevice, etc. In some embodiments, the other nodal devices can use thenodal information 14 to develop analytics that are fed back to theroutine 320 (see adjacent flow routine A to B). In one embodiment, theother nodal devices can use the nodal information 14 to derive nodalanalytics/knowledge 16, 18. In another embodiment, the nodal analytics16 and/or nodal knowledge 18 could be used by a tracker manufacturer todevelop birth certificates (see above) for new tracker installations. Inthis way, newer trackers 200 could be as “smart” as existing trackers200 (i.e., by passing on the same knowledge to new trackers). Forexample, a “new” tracker 200 could learn from an “old” tracker thatoperating at certain motor speeds for a given amount of temperature,humidity, etc., could cause malfunction.

At decision block 325, and if the routine 320 determines that data is tobe pushed (block 323), the routine 320 further determines if it shouldapply at least a portion of the nodal analytics 16. In some embodiments,the routine 320 can delegate more computationally intensive tasks toanother nodal device (e.g., to perform processor intensivecorrelations), but still perform less computationally intensive tasks(e.g., data comparison). In other embodiments, the routine 320 can haveall analytics delegated to another node manager 303.

At block 327 the routine 320 processes nodal information 14 according tovarious nodal analytics 16. An example of such correlations is describedbelow with reference to FIG. 3C.

At block 328 the routine 320 develops nodal knowledge 18 based at leastin part on the nodal analytics 16 applied at block 327. In particular,the routine 320 can decide in a rule making stage whether a particularrule should be tested (or further tested) before it is adopted, whetherthe rule should be discarded, or whether the rule should be deemed true(e.g., an expert opinion rule that is deemed to be fact or that is basedon manual intervention from a user). In one embodiment, certain nodalinformation 14 can cause nodal analytics 16 to substantially change arule.

At decision block 329 the routine 320 determines whether it should grantnodal control to the nodal device. In some embodiments the nodal deviceis passive. For example, the nodal device can be suited to gatheringdata but not controlling the position of the tracker 200. In anotherembodiment, the nodal device could be a consumer device used to gatherdata for analyzing a user power consumption behavior. For example, atransaction could include incentives for users to provide informationrelating to their consumption behavior. A discounted price for such aunit could be offered in exchange for this information. This informationcould be useful for creating nodal knowledge 18, such as consumer powerconsumption. Table 1 provides various other examples of nodalinformation 14.

TABLE 1 Local Nodal Information Generic Nodal Information Wind speedSCADA (e.g., data from grid) Wind direction Grid Voltage Ambienttemperature Grid power factor Barometric pressure Grid configurationAmbient/Planar irradiance (sun Battery charge levels ambient) Availabledischarge power Solar cell temperatures Signal conditioning Motorcurrents User demand Motor temperatures Demand response Tilt/AzimuthUser consumption Real time (time stamp) Consumer pricing GPS location

A fundamental problem with incorporating solar energy into existingelectrical grids is that the grids must have enough capacity to meetpeak demands when aggregate demands are highest. A conventional solutionis “pure peaker” power plants. Conventional solar generation istypically not suitable as a pure peaker because even though itsavailability is predicable, it cannot be guaranteed. Solar generationhas low inertia; generation can be online/offline almostinstantaneously. Conversely traditional power plants (coal, nuclear)require days to cycle. Load following gas plants require 30 to 90minutes. The demand for ancillary services, which is typically overmultiple intervals on any given day, occurs when power must be added toor taken off grid in a matter of seconds to regulate voltage or powerfactor corrections.

For these and other reasons the inclusion of conventional solar systemsat significant levels introduces problems that will increase withadditional solar deployment (PV penetration). Due to both theintermittent and low inertia of conventional solar generation,significant disruption to the generation hierarchy of base load and“pure peaker” plants results in significant price volatility.Additionally, utilities must keep electricity in reserve to provide forsupply disruptions or demand spikes, and they must regulate voltage andkeep it steady. Both imperatives are difficult when solar resources comeonline/offline abruptly.

Referring to FIG. 4A, embodiments of energy distribution systems, orautonomous energy networks (AENs) configured in accordance withembodiments of the present technology can provide a complete solutionfrom deployment to management of a fully autonomous distributed powerplant. The successful realization of an efficient and reliable solarenergy source can include:

-   -   Targeting “nonproductive,” sun-advantaged land close to power        lines for the location of equipment. The ideal property is        suburban/urban brown lots that are on the local substation feed        to where the power will be used. Although, with the use of        virtual net metering, these geographic limitations are bypassed        allowing the generation site and energy consumption to spread        across the utility service area. A low-profile, small footprint        installation facilitates use of marginal nonproductive land and        flat roofs.    -   Solar trackers can enable any solar installation to harvest high        concentrations of solar flux anywhere on the globe. This can        realize, for example, up to a 50% increase over fixed angle        installations for the same solar panels. This increased        production for the same number of solar panels saves on land,        labor, and components costs. Matching production with local        consumption and grid conditions is possible with the addition of        multiple solar trackers. For any solar panel, maximum energy        harvest is guaranteed throughout the year.    -   The tracker electronics 205 can employ a Smart Controller and        Analysis Node (SCAN) that includes an Expert Systems Inference        Engine (ESIE) embedded into each installation to provide        dynamic, rules-based decision response to real-time events on        the grid and at the local installation. Decisions whether to        store, consume power onsite, or net meter are events that need        to take in a myriad of changing conditions.    -   These rules can be generated in the AEN data center to take        advantage of an aggregation of many installations and events        that are happening on the grid (DNR, supply interruptions, real        time pricing, etc.) and then transferred to the local        installation.

To take advantage of high efficiency solar trackers and provide reliablegeneration sources, there can be guaranteed minimum down times and theability to proactively operate and maintain (0 & M) a large number ofinstallations remotely with a minimum of service truck rolls. Each localSCAN installation combined with the AEN can provide installationflexibly with the added sophistication of remote analytics andmanagement. The AEN platform can provide an active intelligent “ExpertSystem” presence at the edge of the grid through, e.g., an integratedcellular communications network.

Each installation can include a computer, such as a Linux basedcomputer, with SCADA (Supervisory Control And Data Acquisition)capabilities and an Expert Systems Inference Engine (ESIE). Thisdecision expertise manages the local installation and provides remoteaccess through, e.g., a cellular network connection. The AEN's SCADA API(Application Program Interface) allows QES, partners, and utilitiesaccess to local grid operational data and a platform for advanced smartgrid applications. The ESIE enhanced supervisory capabilities manage theadvanced power harvesting (Learned Energy Ray Normalization, LERN)algorithms for sun positioning for up to 20 solar trackers. Data isgathered, analyzed, and reported to the AEN network for near real-timeand historical tracking. The ESIE management proactively monitorstrending and exception events that are processed at the local site andreported to the AEN. Modified ESIE rules are downloaded when new“insight” is generated from the AEN master ESIE processing aggregatedata sets of a large number of installations plus third party data bases(real time market pricing, DRM requests, national weather services,solar forecasts, etc.). Various related functions can include:

-   -   Solar PV panels and micro-inverters provide power and data        (inverters; continuous power output and grid voltages). This        data is cached, analyzed, and compressed for upload to the AEN        storage for analysis providing predictive maintenance, warranty,        and billing functions.    -   Voltage control at the point of power consumption. This function        can monitor the voltage and power factor of the grid voltage.        Problems can be rectified, for example, in one or more of the        following ways:        -   Low voltage            -   Add capacitance to correct the power phase of the grid                voltage. This action corrects under voltage conditions                where there is distortion of grid power factor from                large reactive loads (motors, A/C units, etc.).            -   Discharge storage device.        -   High voltage            -   Slightly turning gantry away from sun to reduce                generation.            -   Diverting generation into charging of storage devices.    -   Sensor inputs from the physical actions of the solar tracker and        the local environment can include:        -   Weather station (wind speed, wind direction, temperature,            humidity, solar irradiance, precipitation, barometer,            reference solar irradiance).        -   Spatial orientation sensors of the real world position of            the gantry as opposed to where it is calculated to be. This            sensor can also be used to detect unauthorized gantry            manipulation or attempted theft.        -   Absolute reference sensing of true south compass direction.        -   Additional I/O for controlling local hardware, measuring            local variables, and discrete inputs.

SCADA functions can include:

-   -   Control of each axis motor and correct operation throughout the        controlled sun tracking moves.    -   Measurement of grid voltage.    -   Measurement of grid power factor.    -   Monitor of local controller supply voltage (voltages delivered        to each motor).    -   Status of local network (Modbus, Ethernet, USB, Cellular radio).    -   Monitor and control override switches for local maintenance        operations.    -   iOS (iPhone, iPad, etc.) control surface for local control of        installation.    -   Calculation of the position of the sun.    -   Validate correct movement of the gantry, monitor long term        mechanical drifts of motors and gantry movements, and apply        correction.    -   Logging of power generation and performance metrics, compress,        analyze, and transmit to AEN remote secure server.    -   Update software modules through secure network connection.    -   Provide real-time secure access for troubleshooting.    -   Provide local sun intensity information for cloud prediction        services    -   Interface to existing utility or installed generation and        control equipment.    -   Provide micro climate information, such as current solar        insolation value, temperature, barometric pressure, wind speed,        wind direction, and/or precipitation.

ESIE functions can include:

-   -   Update local ESIE current rule sets.    -   Calculate long term drifts on power generation and apply        corrections (LERN).    -   Provide voltage control action decisions.    -   DMR action decisions.    -   Manage intelligent power storage and dispatch with various        storage and generation options, such as batteries, fuel cells,        and/or compressed Air.    -   Provide intelligent islanding logic, such as automated islanding        reconnection from grid when unstable grid conditions exist,        demand response requests from utility to take site offline on        low voltage (brown out) conditions, intermittent, or blackout        conditions, and/or provide power to on-site customers during        local blackouts.    -   Real time load measurement and management of customer        electricity consumption for energy use profile optimization.        This function can be augmented by use of a local appliance        control network such as ZigBee for granular onsite energy        management.    -   Secure site with motion (gantry) and video surveillance.

In various embodiments, the AEN can include cloud based data and controlnetwork connects to each SCAN installation allowing the aggregation ofinformation and services to be near real time, historical, proactive,and predictive. Each SCAN computer has the intelligence to perform thetasks demanded for complete autonomous operation if it never connects tothe AEN cloud. This can allows for intermittent operations as standaloneentities to optimize various network configurations and real worldlimitations of network performance. Referring to FIG. 4B, in the cloud,there can include a few replication nodes, a number of directory nodes,and a larger number of data nodes. In one implementation, one might runall of these nodes on virtual machines in, e.g., Amazon data centers.Directory nodes can manage information and topology about all othernodes and help nodes find one another. Data nodes store data, andreplication nodes manage pulling data from less-accessible or slowerdata nodes (e.g. tracker nodes) and push data to bigger machines withmore reliable network connectivity.

In normal operation, a solar node collects data and then locates thelocal data node by consulting the directory node. The directory nodealso knows about other directory nodes in the cloud, and can consultthem to find data nodes running in the cloud to push data to as well.Most of the intelligence about where data should go is handled by thereplication nodes in the cloud; the solar node mostly pushes data towhichever is the closest data node according to the directory node.

In disconnected mode, the solar nodes function as before. However, thedirectory nodes cannot find the authoritative directory nodes, so theycoordinate among themselves and elect a gantry to start a localreplication node. This node manages local storage space for the islandby prioritizing data so that the remaining storage is balanced betweendata nodes, and, in the event one needs to discard data, thelowest-priority data is discarded first. Once the network is functioningagain, the replicator node coordinates reconnection to the rest of thenetwork, and the queued-up data drains out to the data nodes in thecloud.

Note that there may be situations where some trackers 200 in a group maybe in a state of permanent disconnection while others in the same grouphave normal network access. In this case, the local replication nodehangs around to replicate data from the partially-disconnected datanodes out to more available data nodes.

The AEN can also provide other features, such as:

-   -   Aggregation and storage of information from all subscribing        installations.    -   Provide API interface to data set for both in-house use and        subscribers:        -   Micro environment weather data over a large geographic area,            providing near real-time and historical information.        -   Near real-time and historical local grid conditions.            -   Voltage            -   Power factor            -   Outage information        -   Near real-time power and historical power generation from            any subscribing SCAN installation.        -   Near real-time and historical power usage of local site            consumption and energy use profiling.        -   Individual SCAN and aggregate solar generation forecasting.        -   DRM service requests.        -   Voltage regulation services.        -   Power factor correction services.    -   Operations & Management of SCAN installations.        -   SCAN ESIE rule sets generation and updating.        -   SCAN program updating.        -   Analyze long term drifts on power output of each PV panel.            -   Expert systems scheduling of service and maintenance.            -   Warranty monitoring for performance guarantees.        -   Power generation metrics for billing of PPA installations.        -   Large account corporate management of multiple sites        -   Utility planning and provisioning

In some embodiments, the knowledge base on the network cloud handleslogistics, the installation process, the sales process, and even thepermitting process to ensure that there is maximum efficiency,reliability, and minimum cost from sales lead generation to maintenanceschedules. This is all exposed to employees and dealers through agraphic interface (see below) that runs on a computer, laptop, tablet orsmartphone. Each employee has a certain level of security clearance thatallows them to see or not to see certain information on their particularaccount.

The SCAN installation can be a complete turnkey solution that providesthe site customer an efficient and reliable power source that isremotely managed and is maintained as an evolving intelligent smart gridpower generation asset. The customer need not have any expertise inpower production equipment, networking, or operations and maintenance.All functions are managed by onsite intelligence supported byintegrated, offsite management.

Smart grid capability built into both the onsite SCAN and remote AENoffer the utility the ability to see what is happening at the customerend of the grid network. These features alleviate the problems of blindsolar deployments (residential rooftops) that offer no visibility to thegrid operator. With the addition of a SCADA API, integration to theutility management software platforms is enabled. This offers real valueto the utility by providing and integrated and reliable power sourcewhere power is consumed. By solving the PV issues articulated above, thePV penetration problem is minimized allowing solar energy to be an assetrather than a problem to the grid.

Referring to FIG. 4C, an AEN controller center can be arranged tooperate various aspects of a microgrid. Referring to FIG. 4D, amicrogrid licensing/investing/purchasing scheme can include technologylicensees, exchange investors, independent generators, independentdealers/franchisers, power purchase agreement providers (PPA), and amicroGrid Energy Independence Exchange. As shown in this scheme, variousnetwork fees, service residuals, licensing fees, investments, anddividends can be generated.

Referring to FIG. 3C, the following disclosure describes certain aspectsof the learning process employed by node managers (e.g., via node nodalanalytics and knowledge). In one embodiment, node managers can gathersensor data from onsite sensors with time stamps and serial numbers.Node managers can further gather third-party data based on the locationsand time stamps of the sensor data. Some examples of possiblethird-party data may include, but are not limited to, one or more of:

a. Utility SCADA requests

b. Weather data

c. Real time energy pricing

d. Grid Battery storage data

The node manager can also determine minimum sample size for 99%confidence using:

${i.\mspace{14mu} n} = \lbrack \frac{Z_{a/2}\sigma}{E} \rbrack^{2}$

-   ii. n=sample size-   iii. Z_(a/2)=critical value=value of standard distribution at 97.5    percentile confidence level-   iv. E=margin of Error    -   1. Simple approximation of margin of error at 99% confidence is:

${E \approx \frac{1.29}{\sqrt{n}}} = \frac{Z_{a/2}\sigma}{\sqrt{n}}$

-   v. σ=standard deviation of x number of time stamped events

${\sigma = {\frac{1}{n}{\sum\limits_{i = 1}^{n}( {x_{i} - \mu} )^{2}}}},{{{where}\mspace{14mu}\mu} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}( x_{i} )}}}$

In one embodiment, if minimal sample size is met for 2 or more datapoints, there can be a test for correlation:

-   -   a. If the absolute value of the correlation coefficient        (determined by the dot product of two value vectors or by the        Pearson product-moment correlation coefficient) is greater than        0.95 then a rule may be made automatically and sent to manual        processing for quality assurance (since correlation does not        always mean causation).        -   i. A theoretical example of this process is as follows:            -   1. Sufficient sample size is met for correlation of 3                data points.            -   2. Correlation coefficient of 0.96 between over voltage                and irradiance levels over 980 W/m² when ambient                temperature is below 84 degrees Fahrenheit.            -   3. A New Rule is made for over voltage correction based                on irradiance levels when temp is below 84 degrees.            -   4. The software model would then test to find weather                conditions that cause 84 degree temperatures and                irradiance levels in excess of 980 W/m² to determine if                over voltage may be forecasted.        -   ii. A theoretical counter example (local rule testing) is as            follows:            -   1. Sufficient sample size is met for correlation of 3                data points.            -   2. Correlation coefficient of 0.74 between under voltage                and battery bank discharge request for 500 kW+/−50 kW on                Saturday between 1 and 3 pm PST in La Jolla, Calif.            -   3. A New Rule is not made.            -   4. Rule is sent for manual processing.            -   5. Manual processing makes a new TEST rule that states:                -   a. Between 1 pm and 3 pm all units within a 20 mile                    radius of La Jolla, Calif. log voltage values and                    test against temperature range.        -   iii. A second theoretical counter example (the case of            statistical insignificance) is as follows:            -   1. Sufficient sample size is met for correlation of 2                data points.            -   2. Correlation coefficient of 0.04 is determined between                demand response requests from utility SCADA on Monday at                5:50 pm local time.            -   3. Correlation is thrown out and determined                statistically insignificant.

The node manager can also employ manual intervention for low correlationcoefficient (greater than 0.05, less than 0.95 correlation coefficientbased on sufficient sample size):

-   -   a. Statistically significant correlations can be manually        examined to further test the correlation.    -   b. Modifications in the rule set for both the network cloud and        the distributed expert systems are used to reach a sufficiently        high correlation coefficient.        -   i. For example: If the correlation coefficient is 0.05, a            manual TEST rule can be implemented to broaden the scope of            correlation in order raise or lower the coefficient.            -   1. If the coefficient stays the same or goes below 0.05,                the correlation is discarded.            -   2. If the coefficient goes up but is still below 0.95 a                new TEST rule can be implemented to broaden the scope of                correlation.

TEST rules are then used by the expert systems to find positivecorrelations, negative correlations, or statistical insignificance. Theutility grid is affected by a statistically infinite number of stimulifrom macro level weather patterns that cause droughts or floods to microlevel human activities like sporting events. This makes manualprocessing important in order to zero in on new rules by expanding thescope of testing when necessary instead of trying to correlate thestatistically infinite number of data points caused by grid stimuli. Forexample, if the grid is experiencing under voltage every weeknightbetween 5:00 pm and 6:00 pm with a correlation coefficient of 0.80, aTEST rule could be implemented to test this correlation vs. differenttemperature ranges. This is because under voltage may only occur at highor low temperatures due to air conditioning or heater use. The 65 to 80degrees Fahrenheit temperature range may not experience under voltage,which may be causing the correlation coefficient to be 0.80. After thenew TEST rule is implemented to check specific temperature ranges, thecorrelation coefficient of under voltage to time of day (5:00-6:00 pm)becomes 0.96. A new rule is made to charge batteries prior to 5:00 pmonly when temperature ranges are lower than 65 or higher than 80 degreesFahrenheit.

TEST rules, in some embodiments, can be predictions, observations,metrics, or desired outcomes that can be tested against events thatoccur. Correlations can be made between TEST rules and mined data fromsensors and third-party databases. Correlation coefficients of less than0.05 are determined statistically insignificant and can be ignored.

Rules can be made for:

-   -   a. How to make rules,    -   b. How to establish a value hierarchy of a set of rules,    -   c. When to log data,    -   d. What data to log,    -   e. How to log, analyze, and correlate data,    -   f. Taking action to solve problems on the grid, the network        cloud management system, or the local sites,    -   g. When to buy electricity from the grid and when to sell power        back to the grid,    -   h. When to charge batteries and when to discharge batteries,    -   i. When to pull off the grid when things become unstable        (islanding),    -   j. How to respond to automated utility requests (i.e. demand        response, microgrid, islanding, etc.),    -   k. When to respond to anomalies (based on standard deviation),        and    -   l. Which predictions, observations, metrics, or desired outcomes        should be cross correlated with mined data.

FIGS. 5A-5E provide various example user interfaces that can be usedwith a node manager. Table 2, below, shows various examples ofapplication interfaces for access with a node manager.

TABLE 2 Tracker control: automatic/manual mode toggle manual positioningdevice calibration device configuration - birth certificates,re-provisioning, latitude/longitude, tracking interval, various otherthings we discover to be useful, knobs for maintenance techs devicesecurity management Access to current/historical tracker log data: panelorientation motor motion statistics (movement errors, exceptionalconditions, etc.) motor amperage computed sun position (later) LERNinformation - how far the sun is from where we computed it to be, andother data collected to refine our solar positioning model powergeneration metrics and other power-related data Access to node/networkevent logs (for diagnostics): related/neighbor nodes local softwareconfiguration (which nodes are running where, node status) data pushed(which data, and to where) rules available for decision-making rulesapplied startup/shutdown/sleep events Real-time and historical smartgrid data access: aggregate grid statistics: power generation, changesthroughout the day, seasons local and aggregate grid performancestatistics “post-mortem” data on how well our tracking and rulesperformed compared to the “ideal model” aggregate statistics bylocation, kind of installation, size of installation, or age ofinstallation

The following description describes certain aspects of InternodalBidding. Each node provides one or more services and/or products thatvary in price based on the supply and demand of those services orproducts. In order to set an accurate market price for transactions thatcan happen frequently (e.g., every millisecond), there must be anautomated system.

In some embodiments, node managers can be accessed through the nodalaccess interface where a minimum price can be set for the services orproducts offered by that particular node. Throughout each day,Internodal Bidding occurs autonomously for each service or product basedon the minimum acceptable value (MAV) for that service or product. Forexample, this could occur when a third party requests a particularservice or product or when the energy network requires a certain serviceor product to maintain reliability.

In some instances, Internodal Bidding can be similar to the way Googleallows users to set a Maximum Cost Per Click (CPC) on certainadvertising. In the Google scenario, users can set parameters for manydifferent outcomes (e.g. maximum exposure of advertisement, lower costper click, a mixture of exposure and cost considerations, etc.) Thosewilling to pay the highest CPC have the best chance of winning many bidsand ensuring the most exposure. Those trying to save money, run the riskof limited or no advertising exposure. Internodal Bidding can occur in asimilar fashion, with the exception that nodes are set to provide aservice or product for a MINIMUM acceptable value. Suppliers of nodesthat are willing to accept a smaller value for their service or productwill likely sell more product or service. For example, if a node has alimited supply, suppliers will likely make their money in times of peakdemand. If a node has a large supply, suppliers may choose to bid verylow in order to sell the most services or products.

One of ordinary skill in the art will recognize that many parametersassociated with cost and demand as well as micro- and macro-transactionscan apply to the concept of Internodal Bidding. In particular examples,Internodal Bidding has several parameters that can be altered or adaptedto allow suppliers associated with nodes to maximize their revenue. Forexample, certain MAV parameters may include setting different MAVs fordifferent times of day, setting different MAVs for different times ofyear, establishing different MAVs for different services or products,establishing different MAVs for different levels of storage capacity,determining different MAVs for different weather patterns, and/orestablishing different MAVs during certain predictable local events(e.g. sports events, festivals, etc.).

In many embodiments, Internodal Bidding can allow for energy productsand services to maintain a reliable service while keeping the cost ofenergy and reliability as low as possible (e.g., much like Google hasdone for the advertising industry). Furthermore, it also allows thelargest energy plants as well as the smallest single energy producer toparticipate in the energy market.

Example

Node 150 contains 64 kW of solar panel capacity and 128 kWh of storage.The Node's MAV for stored kilowatt hours is 60 cents/kWh. This is a veryhigh price per kWh considering that the local retail price ofelectricity is 18 cents/kWh.

The local utility sends out a “spinning reserve” peak demand request for60 kW of capacity for the next 2 hours. Their bid price is 65 cents/kWhbecause the utility's only other option is to cut service at thatparticular substation.

In this example, Node 150 rises to the top of the bidding with its 60cents/kWh bid. Node 150 wins the bid and supplies 120 kWh of capacityfor 60 cents/kWh. Node 150 would have received 8 cents per kWh if soldduring off-peak times or $9.60. Node 150 would have received 18 centsper kWh if sold at retail or $21.60

Because of Automated Internodal Bidding, Node 150 was able to receive$72.00 for just one battery charge cycle. Accordingly, InternodalBidding allows renewable energy and storage devices to provide the sameservices supplied today by fossil fuel powered generators and powerplants in just milliseconds rather than several minutes, hours, or evendays. In particular instances of application, the larger the network ofnodes becomes, the more reliable the products and services can become.

FIG. 6 is flow diagram illustrating an Internodal Bidding scheme 600 inaccordance with an embodiment of the present technology. The scheme 600can include setting a MAV through a Nodal Access Interface (block 602).The scheme 600 can continue at decision block 604 where the scheme 600can determine if the request is manual. If the request is manual, thescheme 600 can continue to block 606 where it is determined if therequest is within bidding parameters (e.g., established parameters,pre-set parameters, etc.). If the request is not manual, the scheme 600can determine if an automated service is necessary (decision block 605).If the automated service is necessary, the scheme 600 can then continueon to block 606 to determine if the request is within biddingparameters. If an automated service is not needed, the scheme 600 canreturn to block 602 where a MAV is set through the Nodal AccessInterface.

If the request is within bidding parameters, the scheme 600 can continueto decision block 608 where it is determined if the bid is low enough.If the bid is low enough, the scheme 600 can provide the product orservice requested (block 610). If the bid exceeds a threshold (e.g., isnot low enough), the scheme 600 can return to block 602 to set a MAVthrough the Nodal Access Interface.

FIG. 7 show various system logs than can be generated by a node managerduring a single day of operation of a solar tracker in accordance withan embodiment of the present technology. Table 3, below, shows examplesetup parameters corresponding to the system logs of FIG. 7.

TABLE 3 A day in the life of a distributed solar power plant deployedinto the Southwest United States. Assumptions: Installed SCANinstallations 3000 Gantries installed 10,800 (average 3.6 gantries perSCAN) Installed Capacity 87.48 MWatt Yearly average capacity factor31.25% (7.5 sun hours, yearly average) Total battery storage capacity45,589 kWh Island enabled SCAN  758 installations Voltage regulation ofinstalled 55% capacity 15 MW (load shedding) Demand/Response capabilityUtility Dispatch Services SCADA Utility API Island demand requestsVoltage control requests Storage discharge requests Load sheddingrequests Utility Data Services: SCAN SCADA Analytic API Solarforecasting data Local grid conditions data (voltage, power factor,outage) Micro climate data sets

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus.

A computer storage medium can be, or can be included in, acomputer-readable storage device, a computer-readable storage substrate,a random or serial access memory array or device, or a combination ofone or more of them. Moreover, while a computer storage medium is not apropagated signal, a computer storage medium can be a source ordestination of computer program instructions encoded in anartificially-generated propagated signal. The computer storage mediumalso can be, or can be included in, one or more separate physicalcomponents or media (e.g., multiple CDs, disks, or other storagedevices). The operations described in this specification can beimplemented as operations performed by a data processing apparatus ondata stored on one or more computer-readable storage devices or receivedfrom other sources.

The term “programmed processor” encompasses all kinds of apparatus,devices, and machines for processing data, including, by way of example,a computer, a system on a chip (or multiple ones or combinations of theforegoing). The apparatus can include special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The apparatus also caninclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. For example, tracker electronics, servers, mobiledevices, etc., can be implemented as a controller in an auxiliarydevice. The processes and logic flows can also be performed by, and theapparatus can also be implemented as, special purpose logic circuitry,e.g., an FPGA or an ASIC. For example, the node manager can beimplemented as a controller in an auxiliary device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. In someembodiments, the processors can be selected according to the type ofdevice.

Generally, a processor will receive instructions and data from aread-only memory or a random access memory or both. The essentialelements of a computer are a processor for performing actions inaccordance with instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. For example, a server cantransfer nodal information, nodal analytics, and/or knowledge can betransferred to flash memory. However, a computer need not have suchdevices. Moreover, a computer can be embedded in another device, e.g., amobile telephone, a personal digital assistant (PDA), a mobile audio orvideo player, a game console, a Global Positioning System (GPS)receiver, or a portable storage device (e.g., a universal serial bus(USB) flash drive), to name just a few. Devices suitable for storingcomputer program instructions and data include all forms of non-volatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., an LCD (liquid crystal display), LED(light emitting diode), or OLED (organic light emitting diode) monitor,for displaying information to the user and a keyboard and a pointingdevice, e.g., a mouse or a trackball, by which the user can provideinput to the computer. In some implementations, a touch screen can beused to display information and to receive input from a user. Otherkinds of devices can be used to provide for interaction with a user aswell; for example, feedback provided to the user can be any form ofsensory feedback, e.g., visual feedback, auditory feedback, or tactilefeedback; and input from the user can be received in any form, includingacoustic, speech, or tactile input. In addition, a computer can interactwith a user by sending documents to and receiving documents from adevice that is used by the user; for example, by sending web pages to aweb browser on a user's client device in response to requests receivedfrom the web browser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

From the foregoing, it will be appreciated that specific embodimentshave been described herein for purposes of illustration, but thatvarious modifications may be made without deviating from the disclosedtechnology. The methods disclosed herein include and encompass, inaddition to methods of making and using the disclosed devices andsystems, methods of instructing others to make and use the discloseddevices and systems. For example, the operating instructions caninstruct the user how to provide any of the operational aspects of theFigures discussed herein. In some embodiments, methods of instructingsuch use and manufacture may take the form ofcomputer-readable-medium-based executable programs or processes.Moreover, aspects described in the context of particular embodiments maybe combined or eliminated in other embodiments. Further, althoughadvantages associated with certain embodiments have been described inthe context of those embodiments, other embodiments may also exhibitsuch advantages, and not all embodiments need necessarily exhibit suchadvantages to fall within the scope of the presently disclosedtechnology.

We claim:
 1. An autonomous energy distribution network, comprising: aplurality of solar tracker devices configured to receive solar energyand transform the solar energy into electrical energy, wherein each ofthe solar tracker devices is directly connected to a node in a powerdistribution grid; a first node manager configured to receive statusinformation from the solar trackers, wherein the status informationincludes information regarding the state of the node to which each ofthe solar tracker device are directly connected, wherein at least one ofthe solar trackers includes the first node manager; and a remotecomputing device including a second node manager in communication withthe first node manager, wherein the second node manager is configured tocontrol operation of the first node manager over a computer network. 2.The energy distribution network of claim 1 wherein the statusinformation further includes geographical location of the solar trackerand a time stamp.
 3. The energy distribution network of claim 2 whereinthe information regarding the state of the node includes power signalcondition.
 4. The energy distribution network of claim 3 wherein thenode manager is configured to associate the power signal condition atindividual nodes with the corresponding geographical location and timestamp of the nodes.
 5. The energy distribution network of claim 1wherein the computer network comprises a communication path thatincludes a cellular link between the first node manager and the secondnode manager.
 6. The energy distribution network of claim 1 wherein thenode manager operates the solar tracker based, at least in part, on thestatus information and without intervention from a remote computingdevice.